Claude Fable 5.1vsGLM-4.6
Claude Fable 5.1 | GLM-4.6 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 355B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1M | 200k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $10.00 | $0.60 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $50.00 | $2.20 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.25 | $0.11 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.43Venice |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.75Venice |
| BenchmarksPublished by one model only | ||
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 81.2% | — |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 68% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 89.1% | — |
SWE-Bench MultimodalMultimodal coding — Real bug reports that arrive with pictures attached — a screenshot, a mockup, a page rendering wrongly — so the AI has to read the image as well as the code to work out what to fix. Higher is better. | 54.7% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 55.8% | — |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 52.6% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 60.9% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 65% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 90% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 31.4% | — |
HealthBench ProfessionalHealth — Realistic health conversations graded against detailed rubrics written by physicians — can the AI respond the way a careful medical professional would? Higher is better. | 62.1% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1853 | — |
AA-BriefcaseKnowledge work — Artificial Analysis agentic office-work eval (Elo) | 1694 | — |
| Overview | ||
| Company | Anthropic | Z.ai |
| Release date | Sep 1 2026 | Sep 30 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
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Frequently asked questions
Claude Fable 5.1 and GLM-4.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4.6 is cheaper on both input and output: $0.60 vs $10.00 per million input tokens, and $2.20 vs $50.00 per million output tokens. GLM-4.6 shipped 336 days before Claude Fable 5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Fable 5.1) vs 200k (GLM-4.6). Claude Fable 5.1 is proprietary, while GLM-4.6 is open weight.
Direct benchmark comparisons are unavailable — Claude Fable 5.1 and GLM-4.6 don't publish scores on any of the same benchmarks.
Claude Fable 5.1 was released by Anthropic on Sep 1 2026.
GLM-4.6 was released by Z.ai on Sep 30 2025.
GLM-4.6 is cheaper on both input and output: $0.60 vs $10.00 per million input tokens, and $2.20 vs $50.00 per million output tokens. Rates are pay-as-you-go API prices verified on September 1, 2026.
Claude Fable 5.1 has a 1M context window; GLM-4.6 has 200k.
Claude Fable 5.1 is a proprietary model released by Anthropic. GLM-4.6 is an open weight model released by Z.ai.